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To make equivalent imagesc, we can use extent [left, right, bottom, top].StepsCreate random data using numpy.Display the data as an image, i.e., on a 2D regular raster, with data and extent [−1, 1, −1, 1] arguments.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True data = np.random.rand(4, 4) plt.imshow(data, extent=[-1, 1, -1, 1]) plt.show()Output
To shade an area between two points in matplotlib, we can take the following steps−Create x and y data points using numpy.Plot x and y data points, with color=red and linewidth=2.To shade an area parallel to X-axis, initialize two variables, y1 and y2.To add horizontal span across the axes, use axhspan() method with y1, y2, green as shade color, and alpha for transprency of the shade.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = np.linspace(0, 20, 500) y = np.cos(3*x) + np.sin(2*x) plt.plot(x, y, c='red', lw=2) ... Read More
To create a graph with date and time in axis labels, we can take the following steps−Create a figure and add a set of subplots.Create x and y data points using numpy.Set date formatter for X-axis.Plot x and y using plot() method.Set the ticks of X-axis.Set the date-time tick labels for X-axis, with some rotation.Make the plot tight layout using plt.tight_layout() method.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt, dates import datetime import numpy as np plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True fig, ax = plt.subplots() x = np.array([datetime.datetime(2021, 1, 1, i, 0) for i ... Read More
To specify values on Y-axis in Python, we can take the following steps−Create x and y data points using numpy.To specify the value of axes, create a list of characters.Use xticks and yticks method to specify the ticks on the axes with x and y ticks data points respectively.Plot the line using x and y, color=red, using plot() method.Make x and y margin 0.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = np.array([0, 2, 4, 6]) y = np.array([1, 3, 5, 7]) ticks = ... Read More
To plot 1D data at a given Y-value with pyplot, we can take the following steps−Initialize y value.Create x and y data points using numpy. zeros_like helps to return an array of zeros with the same shape and type as a given array and add y-value for y data points.Plot x and y with linestyle=dotted, color=red, and linewidth=5.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True y_value = 1 x = np.arange(10) y = np.zeros_like(x) + y_value plt.plot(x, y, ls='dotted', c='red', lw=5) plt.show()OutputRead More
To insert a degree symbol into a plot, we can use LaTeX representation.StepsCreate data points for pV, nR and T using numpy.Plot pV and T using plot() method.Set xlabel for pV using xlabel() method.Set the label for temperature with degree symbol using ylabel() method.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True pV = np.array([3, 5, 1, 7, 10, 9, 4, 2]) nR = np.array([31, 15, 11, 51, 12, 71, 41, 13]) T = np.divide(pV, nR) plt.plot(pV, T, c="red") plt.xlabel("Pressure x Volume") plt.ylabel("Temperature ($^\circ$C)") plt.show()OutputRead More
To get a list of axes of a figure, we will first create a figure and then, use get_axes() method to get the axes and set the labels of those axes.Create xs and ys using numpy and fig using figure() method. Create a new figure, or activate an existing figure.Use add_subplot() method. Add an '~.axes.Axes' to the figure as part of a subplot arrangement, where nrows=1, ncols=1 and index=1.. Get the axes of the fig, and set the xlabel and ylabel.Plot x and y data points with red color.To display the figure, use show() method.Exampleimport numpy as np from matplotlib ... Read More
To get rid of grid lines when plotting with Pandas with secondary_y, we can take the following steps −Create a data frame using DataFrame wth keys column1 and column2.Use data frame data to plot the data frame. To get rid of gridlines, use grid=False.To display the figure, use show() method.Exampleimport pandas as pd from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True data = pd.DataFrame({"column1": [4, 6, 7, 1, 8], "column2": [1, 5, 7, 8, 1]}) data.plot(secondary_y=[5], grid=False) plt.show()Output
To save a file with legend outside the plot, we can take the following steps −Create x data points using numpy.Plot y=sin(x) curve using plot() method, with color=red, marker="v" and label y=sin(x).Plot y=cos(x), curve using plot() method, with color=green, marker="x" and label y=cos(x).To place the legend outside the plot, use bbox_to_anchor(.45, 1.15) and location="upper center".To save the figure, use savefig() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = np.linspace(-2, 2, 100) plt.plot(x, np.sin(x), c="red", marker="v", label="y=sin(x)") plt.plot(x, np.cos(x), c="green", marker="x", label="y=cos(x)") plt.legend(bbox_to_anchor=(.45, 1.15), loc="upper center") plt.savefig("legend_outside.png")OutputWhen we execute this code, it will ... Read More
To change scale of a table, we can use the scale() method. Steps −Create a figure and a set of subplots, nrows=1 and ncols=1.Create a random data using numpy.Make columns value.Make the axis tight and off.Initialize a variable fontsize to change the fontsize.To set the fontsize of the table and to scale the table, we can use 1.5 and 1.5.To display the figure, use the show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig, axs = plt.subplots(1, 1) data = np.random.random((10, 3)) columns = ("Column I", "Column II", "Column III") axs.axis('tight') ... Read More